Dan Jin
Impact in
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- Brain Tumor Detection and Classification
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- Advanced Neuroimaging Techniques and Applications
Papers in
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- Polymer Surface Interaction Studies 2
- Surface Modification and Superhydrophobicity 2
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- Advanced Neuroimaging Techniques and Applications 2
- Advanced MRI Techniques and Applications 2
- Co-authors
- Bing Liu (4 shared papers)Yong Liu (4 shared papers)Zhengyi Yang (3 shared papers)Tianzi Jiang (2 shared papers)Qing Wu (6 shared papers)Kun Zhao (1 shared paper)Jianfeng Zhu (3 shared papers)Yating Li (3 shared papers)
- Journals
- Composites Part B Engineering (2 papers)Cortex (1 paper)International Journal of Biological Macromolecules (1 paper)Journal of Materials Science (1 paper)Chemical Engineering Journal (1 paper)
- Partner nations
- China
In The Last Decade
Dan Jin
10 papers receiving 125 citations
Peers
Comparison fields: 5 of 51
- Neurology 34
- Radiology, Nuclear Medicine and Imaging 45
- Health Information Management 8
- Computational Mathematics 1
- Surfaces, Coatings and Films 11
Countries citing papers authored by Dan Jin
This map shows the geographic impact of Dan Jin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dan Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Jin more than expected).
Fields of papers citing papers by Dan Jin
This network shows the impact of papers produced by Dan Jin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dan Jin. The network helps show where Dan Jin may publish in the future.
Co-authors
The 25 scholars most cited alongside Dan Jin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 43 | |
| 2 | 2020 | 34 | |
| 3 | 2024 | 20 | |
| 4 | 2024 | 13 | |
| 5 | 2025 | 7 | |
| 6 | 2018 | 4 | |
| 7 | 2025 | 2 | |
| 8 | 2019 | 2 | |
| 9 | 2024 | 1 | |
| 10 | 2016 | 1 | |
| 11 | 2025 | 0 |
About Dan Jin
Dan Jin is a scholar working on Surfaces, Coatings and Films, Radiology, Nuclear Medicine and Imaging, Mechanical Engineering, Materials Chemistry and Mechanics of Materials, having authored 11 papers that have together received 127 indexed citations. Recurring topics across this work include Fiber-reinforced polymer composites (3 papers), Advanced Neuroimaging Techniques and Applications (2 papers), Polymer Surface Interaction Studies (2 papers), Graphene research and applications (2 papers), Advanced MRI Techniques and Applications (2 papers), Functional Brain Connectivity Studies (2 papers), Surface Modification and Superhydrophobicity (2 papers) and Machine Learning in Healthcare (1 paper). The work is most often cited by research in Neurology (34 citations), Radiology, Nuclear Medicine and Imaging (45 citations), Health Information Management (8 citations), Computational Mathematics (1 citation) and Surfaces, Coatings and Films (11 citations). Dan Jin has collaborated with scholars based in China. Frequent co-authors include Bing Liu, Yong Liu, Zhengyi Yang, Tianzi Jiang, Qing Wu, Kun Zhao, Jianfeng Zhu, Yating Li, Ningyu An and Pan Wang. Their work appears in journals such as Composites Part B Engineering, Cortex, International Journal of Biological Macromolecules, Journal of Materials Science and Chemical Engineering Journal.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.